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Sep 2, 2022 โ€ข 8 tweets โ€ข 4 min read โ€ข Read on X
For all the devs out there willing to contribute to DVC, here is a quick guide to contributing to iterative/dvc repo
๐Ÿž Open a new issue
๐Ÿ’ป Set up a dev environment
๐Ÿด Fork iterative/dvc
๐Ÿงช Add tests and run them locally
โฌ†๏ธ Submit a pull request
@iterativeai

๐Ÿงต [1/7]
๐Ÿž Open a new issue

Open a new issue in the issue tracker, whether it be a bug report or a feature request. ๐Ÿ‘‡๐Ÿฝ
github.com/iterative/dvc/โ€ฆ

๐Ÿงต[2/7]
๐Ÿด Fork iterative/dvc

Fork iterative/dvc and then clone it into your local computer to start contributing.

๐Ÿงต[3/7]
๐Ÿ’ป Set up a dev environment

Make sure that you have Python 3.8 or higher installed. Install DVC in editable mode with โ€˜pip install -e ".[all,tests]" '.
All this is preferably in a virtual environment.

๐Ÿงต [4/7]
๐Ÿงช Add tests and run them locally

We have unit tests in "tests/unit/" and functional tests in "tests/func/". Consider writing the former to ensure complicated functions and classes behave as expected.
The simplest way to run tests is using the command "python-m tests".

๐Ÿงต [5/7]
Well done ๐Ÿ˜Š. Weโ€™re just about thereโ€ฆ
โฌ†๏ธ Submit a pull request
And finally, submit a pull request, referencing any issues it addresses and get it reviewed and merged. ๐ŸŽ‰

๐Ÿงต[6/7]
โค๏ธThanks for reading

We all could make #DVC more helpful for everyone together ๐Ÿค
Go ahead, fork DVC and try resolving an issue ๐Ÿ‘‡๐Ÿฝ
github.com/iterative/dvc

๐Ÿงต [7/7]

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More from @DVCorg

May 18, 2023
๐—–๐—ผ๐—บ๐—บ๐˜‚๐—ป๐—ถ๐˜๐˜† ๐—ฆ๐—ฝ๐—ผ๐˜๐—น๐—ถ๐—ด๐—ต๐˜!!

We were privileged to have Matt Squire from @FuzzyLabsAI, a renowned expert in the MLOps space, as our guest.

During the session, Matt shared his insights on the benefits of open-source MLOps tools and how they can help businesses.

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In his ๐—ฃ๐—ฟ๐—ฒ๐˜€๐—ฒ๐—ป๐˜๐—ฎ๐˜๐—ถ๐—ผ๐—ป, Matt provided a comprehensive breakdown of the MLOps tool space, categorizing it into SaaS platforms, fully open source, and partly open source tools.
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Matt's views on open-source MLOps tools generated a lot of engagement from our community members.
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May 17, 2023
Several data practitioners have asked for a tutorial video from us on DVC, and we are glad to make this available.

In this video, you'll discover how to use the DVCLive and @huggingface datasets to create a Tweet Sentiment Analyzer.

๐Ÿงต๐Ÿงต๐Ÿงต Image
Do you want to learn more about using real-time metrics in machine learning experiments? Look nowhere else!

In this ๐˜๐˜‚๐˜๐—ผ๐—ฟ๐—ถ๐—ฎ๐—น, we'll show you how to use DVCLive and the Hugging Face Dataset to build a potent Twitter sentiment analyzer.
๐ƒ๐•๐‚๐‹๐ข๐ฏ๐ž, a library from ๐ƒ๐•๐‚, gives you the ability to easily monitor your ML experiments. You will be able to easily grasp the performance of your model at every stage thanks to its real-time metrics.
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Apr 25, 2023
We are happy to announce something exciting to the ML/AI Community๐Ÿšจ

DVC has just released its new integration with Optuna, enabling you to streamline and optimize your hyperparameter search process while keeping track of every step with version control.
Our users have been requesting this integration for a while, and we're thrilled to deliver!

With the DVC's extension for VS Code, you can easily monitor and analyze your results, saving you time and effort in your machine learning workflow.
Try out our new integration today and see the benefits for yourself! ๐Ÿš€

This is the link to the video -

In the video, we explain how to use Optuna with Keras and view each iteration as an experiment in DVC's experiment table and plots. ๐Ÿ”๐Ÿ“ˆ
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Apr 20, 2023
Here's a comprehensive thread on how to build a seamless batch-scoring experience.

You will learn how to:

๐Ÿค– Design ML pipelines with @DVCorg

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๐Ÿงต [1/6]
๐Ÿค– Design #ML pipelines using #DVC! Streamline your process with the following steps:

1๏ธโƒฃ Data prep

2๏ธโƒฃ Feature engineering

3๏ธโƒฃ Model training

4๏ธโƒฃ Model evaluation

DVC optimizes run time & tracks changes for a more efficient workflow!

Get started with 'dvc exp run' ๐Ÿš€

๐Ÿงต [2/6]
Read 14 tweets
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What is your favourite IDE/Code Editor for Machine Learning?

1. Jupyter Lab/Notebook
2. R Studio
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6. Others(name it)
Follow @DVCorg

A lot of people keep mentioning @code

Check out this @code Extension for Machine learning experiment management -

marketplace.visualstudio.com/items?itemNameโ€ฆ
These are the Top @code extensions for Machine learning in 2023.

1. GitHub Copilot
2. DVC
3. GitLens
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6. Python
7. Pylance
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10. Todo MD

You can also share other ones to educate others.
Read 5 tweets
Feb 13, 2023
Woah! Been here? Is deep learning model training going horribly wrong? ๐Ÿ™‹๐Ÿฝโ€โ™‚๏ธ

Iterative Studio makes this easy to see so you don't waste time and resources!

๐Ÿงตย 1/7
With Iterative Studio and DVCLive, you can monitor the progress of your long-running experiments against others that you or your team have performed. All are easily accessed at work, at home, or by the rest of your team on the project.

๐Ÿงต2/7
You provide a couple environment variables for your model training job:

You can enter your STUDIO_TOKEN and dvc exp run if running locally

๐Ÿงต3/7
Read 7 tweets

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